cond.quantile: Conditional quantile

In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

Conditional quantile

Description

Computes the quantile for conditional distribution function.

Usage

cond.quantile(
  qua = 0.5,
  fdata0,
  fdataobj,
  y,
  fn,
  a = min(y),
  b = max(y),
  tol = 10^floor(log10(max(y) - min(y)) - 3),
  iter.max = 100,
  ...
)

Arguments

qua	Quantile value, by default the median (qua=0.5).
fdata0	Conditional functional explanatory data of fdata class object.
fdataobj	Functional explanatory data of fdata class object.
y	Scalar Response.
fn	Conditional distribution function.
a	Lower limit.
b	Upper limit.
tol	Tolerance.
iter.max	Maximum iterations allowed, by default 100.
...	Further arguments passed to or from other methods.

Value

Return the quantile for conditional distribution function.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente [e-mail]

References

Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.

See Also

See Also as: cond.F and cond.mode.

Examples

## Not run: 
n= 100
t= seq(0,1,len=101)
beta = t*sin(2*pi*t)^2
x = matrix(NA, ncol=101, nrow=n)
y=numeric(n)
x0<-rproc2fdata(n,seq(0,1,len=101),sigma="wiener")
x1<-rproc2fdata(n,seq(0,1,len=101),sigma=0.1)
x<-x0*3+x1
fbeta = fdata(beta,t)
y<-inprod.fdata(x,fbeta)+rnorm(n,sd=0.1)

prx=x[1:50];pry=y[1:50]
ind=50+1;ind2=51:60
pr0=x[ind];pr10=x[ind2]
ndist=161
gridy=seq(-1.598069,1.598069, len=ndist)
ind4=5
y0 = gridy[ind4]

# Conditional median
med=cond.quantile(qua=0.5,fdata0=pr0,fdataobj=prx,y=pry,fn=cond.F,h=1)

# Conditional CI 95% conditional
lo=cond.quantile(qua=0.025,fdata0=pr0,fdataobj=prx,y=pry,fn=cond.F,h=1)
up=cond.quantile(qua=0.975,fdata0=pr0,fdataobj=prx,y=pry,fn=cond.F,h=1)
print(c(lo,med,up))

## End(Not run)

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.